Full-scale bridge damage identification using time series analysis of a dense array of geophones excited by drop weight

2015 ◽  
Vol 23 (7) ◽  
pp. 982-997 ◽  
Author(s):  
Reza V. Farahani ◽  
Dayakar Penumadu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Identification ◽  
Full Scale ◽  
Dense Array ◽  
Series Analysis ◽  
Drop Weight ◽  
Bridge Damage

Damage identification using time series analysis and sparse regularization

2020 ◽  
Vol 27 (9) ◽  
Author(s):  
Hongping Zhu ◽  
Hong Yu ◽  
Fei Gao ◽  
Shun Weng ◽  
Yuan Sun ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Identification ◽  
Sparse Regularization ◽  
Series Analysis

Damage Identification of Simply Supported Beam Bridge Based on Time Series Analysis

2012 ◽  
Vol 236-237 ◽  
pp. 617-621
Author(s):  
Han Bing Liu ◽  
Yan Jun Song ◽  
Guo Jin Tan ◽  
Yan Yi Sun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Dynamic Response ◽  
Time Domain ◽  
Damage Identification ◽  
Simply Supported Beam ◽  
Simply Supported ◽  
Series Analysis ◽  
Wide Range ◽  
Beam Bridge

Presently, the study on damage identification of bridges is very popular and it has a wide range of applications. Also the related theory and technology are constantly developing and mature. The researches based on the dynamic response of bridge in frequency domain is more, but the dynamics theory is complex and difficult for the engineering personnel to grasp. On the opposite, although the damage identification method based on the dynamic response of bridge in time domain is easy to understand, it is difficulty for applications. The Auto Regressive Moving Average model (ARMA) of time series analysis can be used to settle this problem. It is a not very abstruse theory and it is already apply for the system identification of some Structures. In this paper, we use time series analysis for the damage identification of simply supported beam bridge combined with its own dynamic response in time domain.

scholarly journals Modeling and Simulation of Biogas Production in Full Scale with Time Series Analysis

2021 ◽  
Vol 9 (2) ◽  
pp. 324
Author(s):  
Celina Dittmer ◽  
Johannes Krümpel ◽  
Andreas Lemmer
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Biogas Production ◽  
Solid Substrate ◽  
Full Scale ◽  
Percentage Error ◽  
Measurement Technology ◽  
Series Analysis ◽  
Substrate Supply ◽  
Extensive Evaluation

Future biogas plants must be able to produce biogas according to demand, which requires proactive feeding management. Therefore, the simulation of biogas production depending on the substrate supply is assumed. Most simulation models are based on the complex Anaerobic Digestion Model No. 1 (ADM1). The ADM1 includes a large number of parameters for all biochemical and physicochemical process steps, which have to be carefully adjusted to represent the conditions of a respective full-scale biogas plant. Due to a deficiency of reliable measurement technology and process monitoring, nearly none of these parameters are available for full-scale plants. The present research investigation shows a simulation model, which is based on the principle of time series analysis and uses only historical data of biogas formation and solid substrate supply, without differentiation of individual substrates. The results of an extensive evaluation of the model over 366 simulations with 48-h horizon show a mean absolute percentage error (MAPE) of 14–18%. The evaluation is based on two different digesters and demonstrated that the model is self-learning and automatically adaptable to the respective application, independent of the substrate’s composition.

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

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